# CommunityGAN: Community Detection with Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1901.06631 · HTML: https://mlsanity.com/paper/1901.06631/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering](https://mlsanity.com/paper/2405.11801/) (2024)
- [Contrastive Graph Clustering in Curvature Spaces](https://mlsanity.com/paper/2305.03555/) (2023)
- [ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Space](https://mlsanity.com/paper/2504.09970/) (2025)
- [Variational Embeddings for Community Detection and Node Representation](https://mlsanity.com/paper/2101.03885/) (2021)
- [Uncovering the Local Hidden Community Structure in Social Networks](https://mlsanity.com/paper/2112.04100/) (2021)
- [Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCN](https://mlsanity.com/paper/2210.11174/) (2022)
- [MGTCOM: Community Detection in Multimodal Graphs](https://mlsanity.com/paper/2211.06331/) (2022)
- [New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics](https://mlsanity.com/paper/2504.15927/) (2025)
- [A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detection](https://mlsanity.com/paper/2505.05965/) (2025)

## Abstract (arXiv)

Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.